Indoor air surveillance and factors associated with respiratory pathogen detection in community settings in Belgium

Currently, the real-life impact of indoor climate, human behaviour, ventilation and air filtration on respiratory pathogen detection and concentration are poorly understood. This hinders the interpretability of bioaerosol quantification in indoor air to surveil respiratory pathogens and transmission risk. We tested 341 indoor air samples from 21 community settings in Belgium for 29 respiratory pathogens using qPCR. On average, 3.9 pathogens were positive per sample and 85.3% of samples tested positive for at least one. Pathogen detection and concentration varied significantly by pathogen, month, and age group in generalised linear (mixed) models and generalised estimating equations. High CO2 and low natural ventilation were independent risk factors for detection. The odds ratio for detection was 1.09 (95% CI 1.03–1.15) per 100 parts per million (ppm) increase in CO2, and 0.88 (95% CI 0.80–0.97) per stepwise increase in natural ventilation (on a Likert scale). CO2 concentration and portable air filtration were independently associated with pathogen concentration. Each 100ppm increase in CO2 was associated with a qPCR Ct value decrease of 0.08 (95% CI −0.12 to −0.04), and portable air filtration with a 0.58 (95% CI 0.25–0.91) increase. The effects of occupancy, sampling duration, mask wearing, vocalisation, temperature, humidity and mechanical ventilation were not significant. Our results support the importance of ventilation and air filtration to reduce transmission.

In May 2020, the test was also validated for SARS-CoV-2 detection (Supplementary Table   11). The SARS-CoV-1/2 qPCR form the respiratory panel, aimed at ORF1ab, was compared against a laboratory developed E-gen RT-qPCR. Both used 5 µl extract and were run on Quantstudio Dx (Thermo Fisher Scientific, Waltham, MA). Tests were run on QCMD samples also used in the Coronavirus disease (COVID-19) outbreak preparedness EQA pilot study 5 , a commercially sourced sample (Qnostics®), a strong positive clinical sample (Wuhan strain) and dilutions from a heat-inactivated SARS-CoV-2 culture from that sample, which was used in a national effort to standardize SARS-CoV-2 diagnosis and reporting by Belgian clinical laboratories 6 . The results are shown in Supplementary Table 11. In all but one of these tests (the Qnostics sample 250 c/ml), the qualitative result was the same for both qPCRs. However, Ct values were consistently higher in the respiratory panel SARS-CoV-2 qPCR, which is consistent with the lower sensitivity of this qPCR than the TaqPath qPCR on air samples in the current study.
Investigating the influence of the qPCR panel on SARS-CoV-2 detection in ambient air samples We included a second, single pathogen, SARS-CoV-2 qPCR (TaqPath) on top of the ORF1ab directed SARS-CoV-1/2 qPCR in the respiratory panel for several reasons. The first was the epidemiological relevance of SARS-CoV-2. Secondly, we knew that the former did not match the latter in terms of Ct values in clinical diagnostics, suggesting a lower sensitivity (Supplementary Table 11). Third, we did have experience screening ambient air for SARS-CoV-2 using the TaqPath assay before the current study, which we did not using the respiratory panel 7 .
We did notice a reduced sensitivity of the SARS-CoV-2 qPCR contained in the respiratory panel in our study, as opposed to the TaqPath qPCR. We investigated this difference using only samples with results for both by means of a McNemar test. A two-sided p-value of ≤0.05 was considered significant in all analyses. We did not correct for multiple testing. See Supplementary Table 10.
Excluding possible non-specific amplification in ambient air samples When we obtained respiratory panel qPCR Ct values above the clinically defined threshold for a particular pathogen (Supplementary Table 12), we repeated the qPCR with fresh primer probe mixes, since an association had been observed between the storage time of frozen aliquoted primer-probe mixes and the number of possible non-specific amplifications in clinical diagnostics. For each pathogen with possible non-specific amplification (air sample Ct values were above the clinically defined threshold), up to five samples with the lowest Ct values were reanalyzed. If at least 60% of samples returned positive, the initial results were retained. If less than 60% of samples returned positive, we reclassified all results of samples with possible nonspecific amplification for that pathogen as negative (Supplementary Table 13).

Imputing missing environmental/building related and behavioural factors
If environmental/building related factors were lost during data collection, we proceeded as follows to impute their values: • If a manual measurement was missing at the start or end of sampling, the corresponding other value was used as average for that sample.
• Missing values for temperature were inferred by computing the mean of measurements in the same location in the same period (7 days before to 7 days after).
Both manual and continuous measurements were included.
When there was no registered value either before or after sampling for mask wearing, natural ventilation or vocalization, the following methods were used consecutively to infer the value: • First, the source notes were checked for information to fill the missing datapoint.
• Second, we computed the mean of measurements in the same location in the same period (7 days before until 7 days after) to infer the value.
• Third, the value was inferred from memory by the data entry team.

Supplementary Figures
Supplementary Figure 1  In most cases, the association is positive between CO2 concentration and the risk of pathogen detection. The association is negative in most instances for natural ventilation.
Counterintuitively, we don't see this expected association between natural ventilation and detection for SARS-CoV-2, even though confidence intervals are quite small across the range.
This may indicate the presence of a strong confounder, such as stricter compliance with nonpharmaceutical interventions when incidence was high. Indeed, the association is not significant in a multivariate analysis (Supplementary Table 5).

Repeated samples in different portable air filtration phases were used as inputs (see Methods).
The change in mean Ct value of all positive respiratory pathogens against the baseline value on Mondays was the model outcome (see also Figure 2). Confidence intervals were calculated using the confint command in R, p-values were obtained using the Kenward-Roger approximation of the t-distribution (pbkrtest package in R).

Remaining variables p-value Effect size (odd ratio and 95% CI) Direction
Pathogen <0.0001 Age group <0.0001  Table 1